{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "\n",
    "%autoreload 2\n",
    "\n",
    "import sys\n",
    "sys.path.append(\"./../..\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/var/pyenv/versions/3.5.2/envs/yb-dev/lib/python3.5/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "%reload_ext yellowbrick\n",
    "%matplotlib inline\n",
    "# Imports\n",
    "import pandas as pd  \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "\n",
    "from sklearn.cross_validation import train_test_split  \n",
    "from sklearn.naive_bayes import BernoulliNB\n",
    "from sklearn.metrics import precision_recall_curve  \n",
    "\n",
    "from yellowbrick.style.palettes import get_color_cycle, PALETTES\n",
    "from yellowbrick.style.colors import resolve_colors\n",
    "from yellowbrick.base import ModelVisualizer\n",
    "from yellowbrick.classifier import ThresholdVisualizer, thresholdviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Retrieve Data Set\n",
    "df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/spambase/spambase.data', header=None)\n",
    "df.rename(columns={57:'is_spam'}, inplace=True)\n",
    "\n",
    "# Build the classifier and get the predictions\n",
    "model = BernoulliNB(3)\n",
    "\n",
    "X = df[[col for col in df.columns if col != 'is_spam']]  \n",
    "y = df['is_spam']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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LAUCy3kziWhVOjz7NrSX/gUMds4RTO8tk7SxpltAOVwiTDmHSJYzbdKJVglabxaK7WMmq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e2b7ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "ThresholdVisualizer(model=None, n_trials=100, quantiles=(0.1, 0.5, 0.9),\n",
       "          random_state=None, test_size_percent=0.1)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viz = ThresholdVisualizer(model, n_trials=100, title=\"Spam vs Ham Thresholds\", quantiles=(0.10, 0.5, .9))\n",
    "viz.fit_show(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KR5v38kFJKcFwy2+rPreTVLeLGDHe1Xv4aPNerM4j3SZ/UAYXTRvNjNE5NDxmw9EYdaEI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\nIZKUBAAhhEhSEgCEECJJ/X/F6ZNMYGFuLwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108a102b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x108a70390>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thresholdviz(model, X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
